This summary table provides a high-level snapshot of system volume for April. With over 612,000 total flights and an average of roughly 19,700 flights per day.
Daily Flights vs 3-Day Rolling Average
This visualization shows the overall flight activity throughout April and compares the daily flight counts to a 3-day rolling average.
The number of flights through out april stay fairly stable around 20,000 flights per day. Daily flight counts fluctuate in a clear weekly pattern: weekday volumes are consistently higher, while weekends show noticeable dips, which explains the repeating troughs in the blue line.
Flight Volume by Day of Week
Code
# Load Problem 3 data: Flight counts by day of weekday_patterns <-read_csv("SQL_exports/Nkemdibe_Okweye_no252_Mini_Project_Problem03.csv",show_col_types =FALSE)# Create ordered factor for daysday_patterns$DayOfWeek <-factor(day_patterns$DayOfWeek,levels =c("Monday", "Tuesday", "Wednesday","Thursday", "Friday", "Saturday", "Sunday"))
This chart shows how flights were distributed across the week in April, revealing which days were the busiest and which saw lighter activity.
While delays frustrate passengers, early departures can be equally problematic as passengers arriving at the scheduled time could miss their flights entirely.
Sample: Dominant Cancellation Reason by Airport (First 10)
Airport Location
Code
Primary Reason
# Cancellations
Dallas/Fort Worth, TX: Dallas/Fort Worth International
B
Weather
888
Chicago, IL: Chicago O'Hare International
B
Weather
844
Denver, CO: Denver International
B
Weather
467
Charlotte, NC: Charlotte Douglas International
B
Weather
382
Minneapolis, MN: Minneapolis-St Paul International
B
Weather
294
Dallas, TX: Dallas Love Field
B
Weather
231
Chicago, IL: Chicago Midway International
B
Weather
218
Houston, TX: William P Hobby
A
Carrier
208
Houston, TX: George Bush Intercontinental/Houston
B
Weather
195
New York, NY: LaGuardia
B
Weather
187
Conclusion
1. Business Travel Dominates the Industry
The pronounced Monday peak (106,575 flights) and Saturday trough demonstrate that business travel, not leisure,. This weekday-centric pattern shows airlines optimize for corporate travelers returning from weekends and beginning weekly business trips.
While most flights operate smoothly, the presence of 43+ hour delays shows the huge gap between routine operations and worst-case scenarios highlighting the industry’s vulnerability.
3. Small Airports Face Disproportionate Challenges
Southwest Oregon Regional Airport’s 47-minute average delay . Smaller regional airports lack the infrastructure, redundancy, and resources to absorb disruptions, leading to persistent performance problems that affect every departure.
4. Weather Remains the Uncontrollable Wild Card
With weather being the dominant cancellation cause across the majority of airports, airlines face an operational reality that no amount of planning can fully overcome. However, carrier-related cancellations represent controllable failures where operational improvements could make a difference.
Recommendations
Operational Improvements
Target Problem Airports: Direct resources to airports with chronic delay issues (OTH, etc.)
Weather Preparedness: Enhance forecasting and contingency planning for April weather patterns
Schedule Buffer: Build in realistic turnaround times to prevent cascading delays
Early Departure Controls: Implement policies to prevent flights from leaving significantly ahead of schedule
Strategic Priorities
Business Traveler Focus: Given Monday’s dominance, optimize Monday operations and customer service
Regional Airport Support: Invest in infrastructure or operational support for struggling smaller airports
Cancellation Transparency: Provide clearer communication about cancellation causes and rebooking options
Data-Driven Resource Allocation: Use airport-specific and airline-specific delay patterns to guide staffing and equipment deployment
Data Source & Methodology
Data Source: U.S. Department of Transportation, Bureau of Transportation Statistics
Dataset: Airline On-Time Performance and Causes of Delay
Time Period: April 2019
Records Analyzed: 600,000+ individual flight records
Analysis Tools: SQL for data querying, R with ggplot2 for visualization